Staff Machine Learning Engineer, Shopping Ads

This is a fully remote position, open to applicants in United States.

📋 Description

• Spearhead the machine learning strategy and framework for the delivery of Shopping Ads, focusing on targeting, retrieval, ranking, engagement, conversion, and value optimization.

• Take ownership of the complete model development process, which includes identifying opportunities, designing data and labels, feature engineering, selecting models, conducting offline evaluations, running online experiments, and overseeing deployment, monitoring, and iteration.

• Create and enhance models aimed at achieving low-funnel advertiser goals while ensuring relevance, user experience, marketplace health, and measurement quality are preserved.

• Formulate strategies for features and representations that link user intent, context, product catalog signals, advertiser signals, and historical interactions.

• Implement and modify cutting-edge machine learning techniques to tackle production challenges.

• Develop systems that strike a balance between prediction quality and online latency, throughput, reliability, operational complexity, and serving costs.

• Lead intricate initiatives across various domains including Shopping Ads, Catalog, Foundational Insights, ML Platform, Ads Serving, Auction, Bidding, Product, and Data Science.

• Establish technical benchmarks through architecture assessments, experimentation protocols, production responsibilities, observability, and model quality standards.

• Guide engineers and technical leads, clarify ownership responsibilities, and facilitate execution in uncertain problem areas.

• Keep abreast of advancements in ad optimization, commerce recommendations, retrieval and ranking techniques, representation learning, and production machine learning systems.

⛳️ Requirements

• Over 7 years of professional experience in software or machine learning engineering, with considerable expertise in developing applied machine learning systems in a production environment.

• Proven experience in constructing end-to-end models or model-driven products that enhance advertising, recommendation, search, or marketplace performance.

• Proficient in optimizing low-funnel objectives, including conversion rates, purchase value, revenue, return on ad spend, or other outcome-centric metrics.

• Extensive hands-on experience in model development, intricate feature engineering, training and evaluation pipelines, online inference, and experimentation.

• A track record of delivering complex outcomes that necessitate collaboration across multiple system components or teams.

• Experience in deploying modern machine learning models in production, achieving significant and measurable performance enhancements.

• Demonstrated technical leadership: guiding direction, driving architecture and execution, mentoring engineers, and influencing cross-functional stakeholders.

• Strong comprehension of large-scale, high-throughput, low-latency machine learning systems and the trade-offs concerning model quality, latency, reliability, and cost.

• Exceptional written and verbal communication, mentoring, and teamwork skills, with the capability to unify teams around a long-term vision for the delivery of Shopping Ads.

• Preferred: background in Shopping Ads, Commerce Ads, Dynamic Product Ads, Product Listing Ads, product recommendations, or retail media.

• Preferred: familiarity with targeting, candidate retrieval, ranking, conversion modeling, value optimization, recommender systems, or representation learning.

• Preferred: experience in designing features or shared representations for multiple models in a multi-stage delivery framework.

• Preferred: knowledge of deep learning architectures, such as multi-task models, sequence models, transformers, two-tower models, graph methods, or learned embeddings.

• Preferred: experience with catalog quality, product feeds, advertiser-side signals, delayed or sparse conversion labels, and online/offline distribution shifts.

• Preferred: experience within a large-scale advertising, social media, search, recommendation, e-commerce, or marketplace enterprise.

🏝️ Benefits

• Comprehensive Healthcare Benefits and Income Replacement Programs

• 401k with Employer Match

• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support

• Family Planning Support

• Gender-Affirming Care

• Mental Health & Coaching Benefits

• Flexible Vacation & Paid Volunteer Time Off

• Generous Paid Parental Leave

• Equity in the form of restricted stock units

• Medical, dental, and vision insurance

• Generous time off for vacation and parental leave